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dc.contributor.authorBlalock, Davis W.
dc.contributor.authorGuttag, John V.
dc.date.accessioned2021-11-08T12:46:30Z
dc.date.available2021-11-08T12:46:30Z
dc.date.issued2016-12
dc.identifier.urihttps://hdl.handle.net/1721.1/137634
dc.description.abstract© 2016 IEEE. Thanks to the rise of wearable and connected devices, sensor-generated time series comprise a large and growing fraction of the world's data. Unfortunately, extracting value from this data can be challenging, since sensors report low-level signals (e.g., acceleration), not the high-level events that are typically of interest (e.g., gestures). We introduce a technique to bridge this gap by automatically extracting examples of real-world events in low-level data, given only a rough estimate of when these events have taken place. By identifying sets of features that repeat in the same temporal arrangement, we isolate examples of such diverse events as human actions, power consumption patterns, and spoken words with up to 96% precision and recall. Our method is fast enough to run in real time and assumes only minimal knowledge of which variables are relevant or the lengths of events. Our evaluation uses numerous publicly available datasets and over 1 million samples of manually labeled sensor data.en_US
dc.language.isoen
dc.publisherIEEEen_US
dc.relation.isversionof10.1109/icdm.2016.0093en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleEXTRACT: Strong Examples from Weakly-Labeled Sensor Dataen_US
dc.typeArticleen_US
dc.identifier.citationBlalock, Davis W. and Guttag, John V. 2016. "EXTRACT: Strong Examples from Weakly-Labeled Sensor Data."
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2019-05-30T14:16:47Z
dspace.date.submission2019-05-30T14:16:48Z
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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